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Seeking Some Special Roadtesters for the UltraZed-EV

rscasny

I am considering to roadtest the UltraZed-EV board. The UltraZed-EV is a highly flexible, rugged, System-On-Module based on the Xilinx Zynq UltraScale+ MPSoC. It's ideal for embedded video processing system development.image

 

In order to roadtest this board, I've discussed with the sponsor what kind of roadtester he needs and any specific requirements as well. Out of those discussions, I've come to the conclusion I need a special roadtester -- someone who has video experience and has the ability to write a quality technical report.

 

Specifically, these are the things the sponsor is looking for in a roadtester:

 

  1. Video Centric (preference to designs using interfaces without an Avnet reference design)
  2. External pieces (Video/PCIe/SATA/SFP+/etc.) should be COTS and readily available
  3. Utilize VCU (optional)
  4. Prefer staying within bounds of VCU TRD (see point 1)
  5. Have fun, show something exciting and new! (AI/ML?)

 

If you are interested and have these requirements, please leave a comment and I'll get back to you. Or, you can message me at

 

 

(BTW: If you are new to roadtesting, and are selected, you would receive the UltraZed-EV at no charge to you. I'll give you more info later.)

 

Sincerely,

 

Randall Scasny

RoadTest Program Manager

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  • I would be interested in testing this board.It looks feature packed.

     

    I would port PYNQ to this board if it's not already. I ported PYNQ to the ZC702:

     

    Porting PYQN to ZC702

    I have posted a couple of  similar projects:without AI.

     

    FPGA camera lane detection with ZYNQ ZC702

    https://www.hackster.io/dhq/fpga-camera-edge-detection-02dce2

     

    Ideally I would use two video feeds to  implement an image captioning neural network (CNN+ LSTM). I see the carrier has HDMI in which is great.

    I have an idea on how to do this on a SOC but I will probably need to use PYNQ  framework to speed up development.

    This will make testing IP cores quite straightforward since I one would have to use Python to talk to them.

     

    Then I would implement a video mixer with multiple cameras. Will use HLS  IP's in order to meet the free cores requirements.

    The MIPI-CSI core is quite expensive. Word on the vine is that Xilinx plans to release it for free in the future Vivado versions.

    so I would use either an HDMi video source, a USB camera which will necessitate Petalinux or use a custom FMC solution with multiple cameras.

    .

    As for AI I think one can either run the DPU V3 .One other option is to compile and use YoloV3 for object localization running on the PS

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  • I would be interested in testing this board.It looks feature packed.

     

    I would port PYNQ to this board if it's not already. I ported PYNQ to the ZC702:

     

    Porting PYQN to ZC702

    I have posted a couple of  similar projects:without AI.

     

    FPGA camera lane detection with ZYNQ ZC702

    https://www.hackster.io/dhq/fpga-camera-edge-detection-02dce2

     

    Ideally I would use two video feeds to  implement an image captioning neural network (CNN+ LSTM). I see the carrier has HDMI in which is great.

    I have an idea on how to do this on a SOC but I will probably need to use PYNQ  framework to speed up development.

    This will make testing IP cores quite straightforward since I one would have to use Python to talk to them.

     

    Then I would implement a video mixer with multiple cameras. Will use HLS  IP's in order to meet the free cores requirements.

    The MIPI-CSI core is quite expensive. Word on the vine is that Xilinx plans to release it for free in the future Vivado versions.

    so I would use either an HDMi video source, a USB camera which will necessitate Petalinux or use a custom FMC solution with multiple cameras.

    .

    As for AI I think one can either run the DPU V3 .One other option is to compile and use YoloV3 for object localization running on the PS

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